In the collection of vibration data, spikes in the data can reduce the reliability of vibration data analysis because a spike could be mistaken as a fault. A data “spike” is defined as a change in a measured value which is unreasonable due to rate of change from previous values, difference in magnitude from previous values, or both. Typically, the “spike” is of short duration, and appears as a very sharp positive or negative excursion on the processed data plot.
Raw vibration data collected by either portable or continuous online vibration devices may be subject to a variety of noise inputs which may manifest as spikes in the processed data. These noise inputs can arise from poor wiring installations, bad cables to vibration sensors, static electric discharges, electromagnetic pickup from external sources such as a noisy electric motor or some other source.
It should be noted that some spikes in the processed data may be legitimate changes in vibration levels or harmonic content due to some sort of fault with the machine being monitored. In general, there are most often other indications (such as a steady increase in trended values) that something is changing in the behavior of the machine.
Spikes in the real-time or historical vibration data can lead to false alarms being generated. Conversely, if historical data is being used to set alarm limits, spikes in the data may result in alarm limits that are too loose, which may result in problems being missed or being alerted to problems too late.
Different techniques have been developed to identify and eliminate spikes from various forms of data. In general, prior art techniques rely on some type of mathematical or statistical analysis to identify outlier data and eliminate a spike as an outlier.
For example, U.S. Pat. No. 7,308,322 discloses a method in which attributes of a motorized system are measured where the attributes are at least one of vibration, speed, temperature, pressure, etc. One step of the method is the elimination of outliers. Certain data patterns are excluded from the data as outliers and extraordinarily large or small data values are typically excluded. US20070260656A1 searches out and subtracts both outlier data and edge data in a method for diagnosing a mechanism. U.S. Pat. No. 7,124,637 discloses a method for determining vibration amplitude limits of a mechanical device. The collected data is corrected using outlier detection procedures that are known in the art. German application DE102010013594A1 discusses measuring outer contours of an object and ignoring individual outliers that may be caused by a speck of dust at the measuring point. U.S. Pat. No. 7,752,012 discloses a method for detecting an abnormal situation associated with the process plant. Specialized data filters and data processing techniques are used to produce enhanced data and such enhanced data may be trimmed to remove outliers. U.S. Pat. No. 9,483,049 B2 discloses an extremely complicated and mathematically intensive method for detecting anomalies in sensed data, including outliers, and rejecting them.
U.S. Pat. No. 4,631,683 discloses a tool that includes a detection system that avoids false alarms caused by noise spikes. EP0018853B1 discloses a technique for discriminating electrical noise spikes from actual impact signals.
These prior art techniques are time consuming to execute and are not well suited for monitoring scalar vibration data from a variety of machines for purposes of preventative maintenance. In other words, these techniques require too much processing time and are slow, and they do not work well in a preventive maintenance environment.